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pro vyhledávání: '"Fernández, Virginia"'
Autor:
Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, Cardoso, M. Jorge
Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there h
Externí odkaz:
http://arxiv.org/abs/2311.04552
Autor:
Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perfor
Externí odkaz:
http://arxiv.org/abs/2307.15208
Autor:
Fernandez, Virginia, Sanchez, Pedro, Pinaya, Walter Hugo Lopez, Jacenków, Grzegorz, Tsaftaris, Sotirios A., Cardoso, Jorge
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without expo
Externí odkaz:
http://arxiv.org/abs/2306.01322
Autor:
Lagunas, Manuel, Impata, Brayan, Martinez, Victor, Fernandez, Virginia, Georgakis, Christos, Braun, Sofia, Bertrand, Felipe
Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently
Externí odkaz:
http://arxiv.org/abs/2305.10018
Autor:
Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Tudosiu, Petru-Daniel, Graham, Mark S, Vercauteren, Tom, Cardoso, M Jorge
In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and governance restrictions, and the costs associated with labelling data,
Externí odkaz:
http://arxiv.org/abs/2209.08256
Autor:
Pinaya, Walter H. L., Tudosiu, Petru-Daniel, Dafflon, Jessica, da Costa, Pedro F, Fernandez, Virginia, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data pr
Externí odkaz:
http://arxiv.org/abs/2209.07162
Autor:
Tudosiu, Petru-Daniel, Pinaya, Walter Hugo Lopez, Graham, Mark S., Borges, Pedro, Fernandez, Virginia, Yang, Dai, Appleyard, Jeremy, Novati, Guido, Mehra, Disha, Vella, Mike, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, Jorge
Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our abi
Externí odkaz:
http://arxiv.org/abs/2209.03177
Autor:
Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Tudosiu, Petru-Daniel, Vercauteren, Tom, Cardoso, M. Jorge
Publikováno v:
In Medical Image Analysis October 2024 97
Autor:
Paula Costa, Ana1 anapcosta@outlook.com, Laura Fernández, Virginia2 virginialaurafernandez@yahoo.com.ar
Publikováno v:
Revista de la CEPAL. ago2024, Issue 143, p89-113. 25p.